A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms

A new model which is capable of generating realistic synthetic phonocardiogram (PCG) signals is introduced based on three coupled ordinary differential equations. The new PCG model takes into account the respiratory frequency, the heart rate variability and the time splitting of first and second heart sounds. This time splitting occurs with each cardiac cycle and varies with inhalation and exhalation. Clinical PCG statistics and the close temporal relationship between events in ECG and PCG are used to deduce values of PCG model parameters.In comparison with published PCG models, the proposed model allows a larger number of known PCG features to be taken into consideration. Moreover it is able to generate both normal and abnormal realistic synthetic heart sounds. Results show that these synthetic PCG signals have the closest features to those of a conventional heart sound in both time and frequency domains. Additionally, a sound quality test carried out by eight cardiologists demonstrates that the proposed model outperforms the existing models.This new PCG model is promising and useful in assessing signal processing techniques which are developed to help clinical diagnosis based on PCG.     

Heart energy signature spectrogram for cardiovascular diagnosis

A new method and application is proposed to characterize intensity and pitch of human heart sounds and murmurs. Using recorded heart sounds from the library of one of the authors, a visual map of heart sound energy was established. Both normal and abnormal heart sound recordings were studied. Representation is based on Wigner-Ville joint time-frequency transformations. The proposed methodology separates acoustic contributions of cardiac events simultaneously in pitch, time and energy. The resolution accuracy is superior to any other existing spectrogram method. The characteristic energy signature of the innocent heart murmur in a child with the S3 sound is presented. It allows clear detection of S1, S2 and S3 sounds, S2 split, systolic murmur, and intensity of these components. The original signal, heart sound power change with time, time-averaged frequency, energy density spectra and instantaneous variations of power and frequency/pitch with time, are presented. These data allow full quantitative characterization of heart sounds and murmurs. High accuracy in both time and pitch resolution is demonstrated. Resulting visual images have self-referencing quality, whereby individual features and their changes become immediately obvious.     

Segmentation of heart sounds based on dynamic clustering

The heart sound signal is first separated into cycles, where the cycle detection is based on an instantaneous cycle frequency. The heart sound data of one cardiac cycle can be decomposed into a number of atoms characterized by timing delay, frequency, amplitude, time width and phase. To segment heart sounds, we made a hypothesis that the atoms of a heart sound congregate as a cluster in time–frequency domains. We propose an atom density function to indicate clusters. To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of heart sounds. Therefore, heart sounds are indicated by the hybrid analysis of clustering and medical knowledge. The segmentation scheme is automatic and no reference signal is needed. Twenty-six subjects, including 3 normal and 23 abnormal subjects, were tested for heart sound signals in various clinical cases. Our statistics show that the segmentation was successful for signals collected from normal subjects and patients with moderate murmurs.     

Bayesian denoising framework of phonocardiogram based on a new dynamical model

In this paper, we introduce a model-based Bayesian denoising framework for phonocardiogram (PCG) signals. The denoising framework is founded on a new dynamical model for PCG, which is capable of generating realistic synthetic PCG signals. The introduced dynamical model is based on PCG morphology and is inspired by electrocardiogram (ECG) dynamical model proposed by McSharry et al. and can represent various morphologies of normal PCG signals. The extended Kalman smoother (EKS) is the Bayesian filter that is used in this study. In order to facilitate the adaptation of the denoising framework to each input PCG signal, the parameters are selected automatically from the input signal itself. This approach is evaluated on several PCGs recorded on healthy subjects, while artificial white Gaussian noise is added to each signal, and the SNR and morphology of the outputs of the proposed denoising approach are compared with the outputs of the wavelet denoising (WD) method. The results of the EKS demonstrate better performance than WD over a wide range of PCG SNRs. The new PCG dynamical model can also be employed to develop other model-based processing frameworks such as heart sound segmentation and compression.     

Comparison of first and second heart sounds after mechanical heart valve replacement

In this article, the spectral features of first heart sounds (S1) and second heart sounds (S2), which comprise the mechanical heart valve sounds obtained after aortic valve replacement (AVR) and mitral valve replacement (MVR), are compared to find out the effect of mechanical heart valve replacement and recording area on S1 and S2. For this aim, the Welch method and the autoregressive (AR) method are applied on the S1 and S2 taken from 66 recordings of 8 patients with AVR and 98 recordings from 11 patients with MVR, thereby yielding power spectrum of the heart sounds. Three features relating to frequency of heart sounds and three features relating to energy of heart sounds are obtained. Results show that in comparison to natural heart valves, mechanical heart valves contain higher frequency components and energy, and energy and frequency components do not show common behaviour for either AVR or MVR depending on the recording areas. Aside from the frequency content and energy of the sound generated by mechanical heart valves being affected by the structure of the lungs–thorax and the recording areas, the pressure across the valve incurred during AVR or MVR is a significant factor in determining the frequency and energy levels of the valve sound produced. Though studies on native heart sounds as a non-invasive diagnostic method has been done for many years, it is observed that studies on mechanical heart valves sounds are limited. The results of this paper will contribute to other studies on using a non-invasive method for assessing the mechanical heart valve sounds.     

Discovery of multiple level heart-sound morphological variability resulting from changes in physiological states

Heart sounds carry information about the mechanical activity of the cardiovascular system. This information includes the specific physiological state of the subject, and short term variability related to the respiratory cycle. The interpretation of the sounds and extraction of changes in the physiological state, while monitoring short term variability is still an open problem and is the subject of this paper.We present a novel computational framework for analysis of data with multi-level variability, caused by externally induced changes. The framework presented includes an initial clustering of the first heart sound (S1) according to the morphology, and further aggregation of clusters into super-clusters. The clusters and super clusters are two methods of data segmentation, each reflecting a different level of variability in the data.The framework is applied to heart sounds recorded during laparoscopic surgeries of six patients. Procedures of this kind include anesthesia and abdominal insufflation, which together with the respiratory cycle, induce changes to the heart sound signal. We demonstrate a separation of the heart sound morphology according to different physiological states. The physiological states considered are the respiratory cycle, and the stages of the surgery. We achieve results of 90 ± 4% classification accuracy of heart beats to operation stages.The proposed framework is general and can be used to analyze data characterized by multi-level variability for various other (biomedical) applications.     

An algorithm for FHR estimation from foetal phonocardiographic signals

The long-term foetal surveillance is often to be recommended. Hence, the fully non-invasive acoustic recording, through maternal abdomen, represents a valuable alternative to the ultrasonic cardiotocography. Unfortunately, the recorded heart sound signal is heavily loaded by noise, thus the determination of the foetal heart rate raises serious signal processing issues. In this paper, we present a new algorithm for foetal heart rate estimation from foetal phonocardiographic recordings. A filtering is employed as a first step of the algorithm to reduce the background noise. A block for first heart sounds enhancing is then used to further reduce other components of foetal heart sound signals. A complex logic block, guided by a number of rules concerning foetal heart beat regularity, is proposed as a successive block, for the detection of most probable first heart sounds from several candidates. A final block is used for exact first heart sound timing and in turn foetal heart rate estimation. Filtering and enhancing blocks are actually implemented by means of different techniques, so that different processing paths are proposed. Furthermore, a reliability index is introduced to quantify the consistency of the estimated foetal heart rate and, based on statistic parameters; [,] a software quality index is designed to indicate the most reliable analysis procedure (that is, combining the best processing path and the most accurate time mark of the first heart sound, provides the lowest estimation errors). The algorithm performances have been tested on phonocardiographic signals recorded in a local gynaecology private practice from a sample group of about 50 pregnant women. Phonocardiographic signals have been recorded simultaneously to ultrasonic cardiotocographic signals in order to compare the two foetal heart rate series (the one estimated by our algorithm and the other provided by cardiotocographic device). Our results show that the proposed algorithm, in particular some analysis procedures, provides reliable foetal heart rate signals, very close to the reference cardiotocographic recordings.     

Hemodynamics-driven mathematical model of first and second heart sound generation

We propose a novel, two-degree of freedom mathematical model of mechanical vibrations of the heart that generates heart sounds in CircAdapt, a complete real-time model of the cardiovascular system. Heart sounds during rest, exercise, biventricular (BiVHF), left ventricular (LVHF) and right ventricular heart failure (RVHF) were simulated to examine model functionality in various conditions. Simulated and experimental heart sound components showed both qualitative and quantitative agreements in terms of heart sound morphology, frequency, and timing. Rate of left ventricular pressure (LV dp/dt_max) and first heart sound (S1) amplitude were proportional with exercise level. The relation of the second heart sound (S2) amplitude with exercise level was less significant. BiVHF resulted in amplitude reduction of S1. LVHF resulted in reverse splitting of S2 and an amplitude reduction of only the left-sided heart sound components, whereas RVHF resulted in a prolonged splitting of S2 and only a mild amplitude reduction of the right-sided heart sound components. In conclusion, our hemodynamics-driven mathematical model provides fast and realistic simulations of heart sounds under various conditions and may be helpful to find new indicators for diagnosis and prognosis of cardiac diseases.New & noteworthyTo the best of our knowledge, this is the first hemodynamic-based heart sound generation model embedded in a complete real-time computational model of the cardiovascular system. Simulated heart sounds are similar to experimental and clinical measurements, both quantitatively and qualitatively. Our model can be used to investigate the relationships between heart sound acoustic features and hemodynamic factors/anatomical parameters.     

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Autonomous acoustic recorders are an increasingly popular method for low‐disturbance, large‐scale monitoring of sound‐producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time‐synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time‐synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor‐intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open‐source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low‐disturbance, large‐scale collection of animal position data.     

PCG-Cut: Graph Driven Segmentation of the Prostate Central Gland

Prostate cancer is the most abundant cancer in men, with over 200,000 expected new cases and around 28,000 deaths in 2012 in the US alone. In this study, the segmentation results for the prostate central gland (PCG) in MR scans are presented. The aim of this research study is to apply a graph-based algorithm to automated segmentation (i.e. delineation) of organ limits for the prostate central gland. The ultimate goal is to apply automated segmentation approach to facilitate efficient MR-guided biopsy and radiation treatment planning. The automated segmentation algorithm used is graph-driven based on a spherical template. Therefore, rays are sent through the surface points of a polyhedron to sample the graph’s nodes. After graph construction – which only requires the center of the polyhedron defined by the user and located inside the prostate center gland – the minimal cost closed set on the graph is computed via a polynomial time s-t-cut, which results in the segmentation of the prostate center gland’s boundaries and volume. The algorithm has been realized as a C++ module within the medical research platform MeVisLab and the ground truth of the central gland boundaries were manually extracted by clinical experts (interventional radiologists) with several years of experience in prostate treatment. For evaluation the automated segmentations of the proposed scheme have been compared with the manual segmentations, yielding an average Dice Similarity Coefficient (DSC) of 78.94±10.85%.     

Detection of heart murmurs using wavelet analysis and artificial neural networks

This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.     

Localized perforation of the cell wall by a major autolysin: atl gene products and the onset of penicillin-induced lysis of Staphylococcus aureus.

We investigated the cell surface localization of the atl gene products of Staphylococcus aureus exposed to a lytic concentration (4 MIC) of penicillin G (PCG) by means of immunoelectron microscopy using anti-62-kDa N-acetylmuramyl-L-alanine amidase or anti-51-kDa endo-beta-N-acetylglucosaminidase immunoglobulin G. Protein A-gold conjugates reacting with antigen-antibody complex localized at sites of defects of the cell wall at the nascent cross wall. Anti-62-kDa N-acetylmuramyl-L-alanine amidase or anti-51-kDa endo-beta-N-acetylglucosaminidase immunoglobulin G inhibited the decreased turbidity caused by PCG-induced lysis and the formation of defects in the wall. The autolysis-defective mutant, S. aureus RUSAL2 (atl::Tn551), exposed to 4 MIC of PCG resisted autolysis and formation of the wall defect. These results suggest that activation or deregulation of the atl gene products at localized sites where formation of new cross wall was disturbed by PCG causes small defects in the cell wall in situ, eventually leading to general autolysis.     

A Humanoid Method for Extracting Abnormal Engine Sounds from Engine Acoustics Based on Adaptive Voiterra Filter

The improvement of SNR (Signal-to-Noise Ratio) of abnormal engine sounds is of great help in improving the accuracy of engine fault diagnosis.By imitating the way that human technicians use to distinguish abnormal engine sounds from engine acoustics,a humanoid abnormal sound extracting method is proposed.By implementing adaptive Volterra filter in the canonical Adaptive Noise Cancellation (ANC) system,the proposed method is capable of tracing the engine baseline sound which exhibits an intrinsic nonlinear dynamics.Besides,by introducing a template noise tailored from the records of engine baseline sound and taking it as virtual input of the adaptive Volterra filter,the priori knowledge of engine baseline sound,such as inherent correlation,periodicity or phase information,and stochastic factors,is taken into consideration.The hybrid simulations prove that the proposed method is functional.Since the method proposed is essentially a single-sensor based ANC,hopefully,it may become an effective way to extricate the dilemma that canonical dual-sensor based ANC encounters when it is used in extracting fault-featured signals from observed signals.     

Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform

The automatic segmentation of cardiac sound signals into heart beat cycles is generally required for the diagnosis of heart valve disorders. In this paper, a new method for segmentation of the cardiac sound signals using tunable-Q wavelet transform (TQWT) has been presented. The murmurs from cardiac sound signals are removed by suitably constraining TQWT based decomposition and reconstruction. The Q-factor, redundancy parameter and number of stages of decomposition of the TQWT are adapted to the desired statistical properties of the murmur-free reconstructed cardiac sound signals. The envelope based on cardiac sound characteristic waveform (CSCW) is extracted after the removal of low energy components from the reconstructed cardiac sound signals. Then the heart beat cycles are derived from the original cardiac sound signals by mapping the required timing information of CSCW which is obtained using established methods. The experimental results are included in order to show the effectiveness of the proposed method for segmentation of cardiac sound signals in comparison with other existing methods for various clinical cases.     

High binaural coherence determines successful sound localization and increased activity in posterior auditory areas

Our brain continuously receives complex combinations of sounds originating from different sources and relating to different events in the external world. Timing differences between the two ears can be used to localize sounds in space, but only when the inputs to the two ears have similar spectrotemporal profiles (high binaural coherence). We used fMRI to investigate any modulation of auditory responses by binaural coherence. We assessed how processing of these cues depends on whether spatial information is task relevant and whether brain activity correlates with subjects' localization performance. We found that activity in Heschl's gyrus increased with increasing coherence, irrespective of whether localization was task relevant. Posterior auditory regions also showed increased activity for high coherence, primarily when sound localization was required and subjects successfully localized sounds. We conclude that binaural coherence cues are processed throughout the auditory cortex and that these cues are used in posterior regions for successful auditory localization.     

Mutations in CYP1B1, the gene for cytochrome P4501B1, are the predominant cause of primary congenital glaucoma in Saudi Arabia.

The autosomal recessive disorder primary congenital glaucoma (PCG) is caused by unknown developmental defect(s) of the trabecular meshwork and anterior chamber angle of the eye. Homozygosity mapping with a DNA pooling strategy in three large consanguineous Saudi PCG families identified the GLC3A locus on chromosome 2p21 in a region tightly linked to PCG in another population. Formal linkage analysis in 25 Saudi PCG families confirmed both significant linkage to polymorphic markers in this region and incomplete penetrance, but it showed no evidence of genetic heterogeneity. For these 25 families, the maximum combined two-point LOD score was 15.76 at a recombination fraction of .021, with the polymorphic marker D2S177. Both haplotype analysis and homozygosity mapping in these families localized GLC3A to a 5-cM critical interval delineated by markers D2S2186 and D2S1356. Sequence analysis of the coding exons for cytochrome P4501B1 (CYP1B1) in these 25 families revealed three distinctive mutations that segregate with the phenotype in 24 families. Additional clinical and molecular data on some mildly affected relatives showed variable expressivity of PCG in this population. These results should stimulate a study of the genetic and environmental events that modify the effects of CYP1B1 mutations in ocular development. Furthermore, the small number of PCG mutations identified in this Saudi population makes both neonatal and population screening attractive public health measures.     

A Dominance Hierarchy of Auditory Spatial Cues in Barn Owls

Background

Barn owls integrate spatial information across frequency channels to localize sounds in space.

Methodology/Principal Findings

We presented barn owls with synchronous sounds that contained different bands of frequencies (3–5 kHz and 7–9 kHz) from different locations in space. When the owls were confronted with the conflicting localization cues from two synchronous sounds of equal level, their orienting responses were dominated by one of the sounds: they oriented toward the location of the low frequency sound when the sources were separated in azimuth; in contrast, they oriented toward the location of the high frequency sound when the sources were separated in elevation. We identified neural correlates of this behavioral effect in the optic tectum (OT, superior colliculus in mammals), which contains a map of auditory space and is involved in generating orienting movements to sounds. We found that low frequency cues dominate the representation of sound azimuth in the OT space map, whereas high frequency cues dominate the representation of sound elevation.

Conclusions/Significance

We argue that the dominance hierarchy of localization cues reflects several factors: 1) the relative amplitude of the sound providing the cue, 2) the resolution with which the auditory system measures the value of a cue, and 3) the spatial ambiguity in interpreting the cue. These same factors may contribute to the relative weighting of sound localization cues in other species, including humans.     

Multi-Scale and Shape Constrained Localized Region-Based Active Contour Segmentation of Uterine Fibroid Ultrasound Images in HIFU Therapy

Purpose

To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.

Methods

We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.

Results

Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).

Conclusions

An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.